Explanation
Javascript Program
R Codes
Sample Size Table
E
F
This page presents 4 programs related to sample size requirements when comparing 2 proportions, and tables of these sample size.
The programs and tables on this page assumes that the disfference between 2 proportions has a normal distribution, so is mainly for comparing risk difference using the Standard model.
However, as the risk difference (Exact model), risk ratio, and odds ratio have similar powers, the results from this page are also applicable to these models.
However, the use of odds ratio in the retrospective matched pair controlled studies is different, and sample size for these are separately calculated (see Subject Index)
if a researcher wishes to use a smaller sample size than those produced by the programs on this page or its tables, he/she should use either the Chi Squares Test for the 2x2 contingency table or the Fisher's Exact Probability (see Subject Index), as these do not assume a normal transformation of the difference between proportions. The requirements of these program are discussed in their own page
The following 4 programs are available on this page
- Sample Size requires the following input
- The probability of Type I Error (p, α) that will be used to determine significance. This is usually 0.05, but 0.1, 0.01, 1nd 0.001 are also used
- The power (1 - β) required. Usually this is 0.8, and sometimes 0.9
- The expected proportions in the two groups (P1 and P2)
The results is the sample size in each of the 2 groups, assuming that they are equal. Sample size for the 1 and 2 tail models are produced.
For example, if α of 0.05 and power of 0.8 are required, and the two proportions are expected to be 5% (0.05) and 10% (0.1), then the sample size of 343 cases per group is required for the 1 tail model, and 435 for the 2 tail model
- Power requires the following imput
- The probability of Type I Error (p, α) that will be used to determine significance. This is usually 0.05, but 0.1, 0.01, 1nd 0.001 are also used
- The sample size and proportion found in group 1 (N1, P1) and group 2 (N2, P2)
The results are the powers of the comparison, 1 and 2 tail models.
For example, if α of 0.05 is to be used to determine statistical significance, the sample size in the two groups are (N1=76 and N2=78), and the two proportions found are 5% (P1=0.05) ans 17% (P2=0.17), then the powers of the comparison are 0.93 for the 1 tail model and 0.89 for the 2 tail model
- Confidence Interval (CI) reuires the following input
- The percentage representing the level of confidence reqwuired. This is usually 95%, but sometimes 99% is used
- The sample size and proportion found in group 1 (N1, P1) and group 2 (N2, P2)
The results are the confidence interval of the difference, 1 and 2 tail
For example, if the sample size in the two groups are (N1=76 and N2=78), and the two proportions found are 5% (P1=0.05) ans 17% (P2=0.17), then the 95% confidence intervals are
- For the 1 tail model, > -0.20 (-20%) or <-0.04 (-4%)
- For the two tail model, -0.22 (-22%) to -02 (-2%)
- Pilot Studies requires the following parameters
| 1 Tail | 2 Tail |
|---|
| Ssiz | CI1 | Diff | Dec/case | %Dec/cas |
"CI | Diff | Dec/case | %Dec/cas |
|---|
| 5 | 0.999 | | 1.1904 | |
| 10 | 0.7064 | 0.2926 | 0.0585 | 6.0 |
0.8417 | 0.3487 | 0.0697 | 6.0 |
| 15 | 0.5768 | 0.1296 | 0.0259 | 4.0 |
0.6873 | 0.1545 | 0.0309 | 4.0 |
| 20 | 0.4995 | 0.0773 | 0.0155 | 3.0 |
0.5952 | 0.0921 | 0.0184 | 3.0 |
| 25 | 0.4468 | 0.0527 | 0.0105 | 2.0 |
0.5324 | 0.0628 | 0.0126 | 2.0 |
| 30 | 0.4078 | 0.0389 | 0.0078 | 2.0 |
0.486 | 0.0464 | 0.0093 | 2.0 |
| 35 | 0.3776 | 0.0303 | 0.0061 | 1.0 |
0.4499 | 0.036 | 0.0072 | 1.0 |
| 40 | 0.3532 | 0.0244 | 0.0049 | 1.0 |
0.4209 | 0.0291 | 0.0058 | 1.0 |
| 45 | 0.333 | 0.0202 | 0.004 | 1.0 |
0.3968 | 0.0241 | 0.0048 | 1.0 |
| 50 | 0.3159 | 0.0171 | 0.0034 | 1.0 |
0.3764 | 0.0204 | 0.0041 | 1.0 |
- The % of confidence, which is usually 95 or 99%. This is converted to probability of Type I error (α)
- The expected proportions in the two groups (P1 and P2)
- The interval of sample size (intv) to exaamine changes in confidence interval as sample sizw=es increase. Usually this is between 3 and 10 cases
- The maximum sample size per group for the estimates. In most cases, pilot studies end in 30 to 40 cases, and there is no point having a pilot study with more than 100 cases per group. A common value used is 50
The program produces a table as shown to the right, listing the confidence intervals (1 and 2 tails). With increasing sample size, the reduction in confidence interval decreases, and it can be seen that beyond 30 cases per group, the further decrease in confidence interval become trivial. A conclusion can therefore be made that a sample size of 30 cases per group would be suitable for a pilot study, to define the expected proportions, and provide information on the research environment, so that a formal comparison can be planned.
References
Machin D, Campbell M, Fayers, P, Pinol A (1997) Sample Size Tables for Clinical Studies. Second Ed. Blackwell Science IBSN 0-86542-870-0 p. 18-20
Johanson GA and Brooks GP (2010) Initial Scale Development: Sample Size for Pilot Studies. Educational and Psychological Measurement Vol.70,Iss.3;p.394-400
Sample Size
Power
Confidence Interval
Pilot Study
E
F
G
# Sample Size
# data entry
dat = ("
Alpha Power P1 P2
0.05 0.8 0.05 0.1
0.01 0.8 0.05 0.1
0.05 0.9 0.05 0.1
0.01 0.9 0.05 0.1
")
df <- read.table(textConnection(dat),header=TRUE) # conversion to data frame
# vectors for results
SSiz1Tail <- vector()
SSiz2Tail <- vector()
# Calculate sample size
for(i in 1 : nrow(df))
{
alpha = df$Alpha[i]
beta = 1 - df$Power[i]
p1 = df$P1[i]
p2 = df$P2[i]
ra = 1
pm = (p1 + ra * p2) / (1 + ra) # mean prop
delta = abs(p1 - p2)
zb = qnorm(beta)
za = qnorm(alpha / 1) # 1 tail za
top = za * sqrt((1 + ra) * pm * (1 - pm)) + zb * sqrt(ra * p1 * (1 - p1) + p2 * (1 - p2))
SSiz1Tail <- append(SSiz1Tail, ceiling(top^2 / (ra * delta^2)))
za = qnorm(alpha / 2) # 2 tail za
top = za * sqrt((1 + ra) * pm * (1 - pm)) + zb * sqrt(ra * p1 * (1 - p1) + p2 * (1 - p2))
SSiz2Tail <- append(SSiz2Tail, ceiling(top^2 / (ra * delta^2)))
}
df$SSiz1Tail <- SSiz1Tail
df$SSiz2Tail <- SSiz2Tail
df # input data and sample size per group
The results are sample size per group for 1 and 2 tail models
> df # input data and sample size per group
Alpha Power P1 P2 SSiz1Tail SSiz2Tail
1 0.05 0.8 0.05 0.1 343 435
2 0.01 0.8 0.05 0.1 556 647
3 0.05 0.9 0.05 0.1 474 582
4 0.01 0.9 0.05 0.1 721 824
# Power
# data entry
dat = ("
Alpha N1 P1 N2 P2
0.05 76 0.05 78 0.17
0.01 113 0.05 115 0.22
0.05 101 0.05 100 0.21
0.01 143 0.05 140 0.19
")
df <- read.table(textConnection(dat),header=TRUE) # conversion to data frame
df
# Vectors for results
Power1Tail <- vector()
Power2Tail <- vector()
# Calculations
for(i in 1 : nrow(df))
{
alpha = df$Alpha[i]
n1 = df$N1[i]
p1 = df$P1[i]
n2 = df$N2[i]
p2 = df$P2[i]
ra = n2 / n1
pm = (p1 + ra * p2) / (1 + ra) # mean prop
delta = abs(p1 - p2)
za = abs(qnorm(alpha / 1)) # 1 tail
zb = (delta * sqrt(ra * n1) - za * sqrt((1 + ra) * pm * (1 - pm))) / sqrt(ra * p1 * (1 - p1) + p2 * (1 - p2))
Power1Tail <- append(Power1Tail,pnorm(zb))
za = abs(qnorm(alpha / 2)) # 2 tail
zb = (delta * sqrt(ra * n1) - za * sqrt((1 + ra) * pm * (1 - pm))) / sqrt(ra * p1 * (1 - p1) + p2 * (1 - p2))
Power2Tail <- append(Power2Tail,pnorm(zb))
}
# include results to data frame for display
df$Power1Tail <- Power1Tail
df$Power2Tail <- Power2Tail
df # dataframw with data entry and results (power 1 and 2 tail)
The results are as follows
- Alpha = probability of Type I Error (α, p)
- N1, P1 = sample size and probability of group 1
- N2, P2 = sample size and probability of group 2
- Power = power, 1 and 2 tail
> df # dataframw with data entry and results (power 1 and 2 tail)
Alpha N1 P1 N2 P2 Power1Tail Power2Tail
1 0.05 76 0.05 78 0.17 0.7720891 0.6637205
2 0.01 113 0.05 115 0.22 0.9297388 0.8878224
3 0.05 101 0.05 100 0.21 0.9623354 0.9271517
4 0.01 143 0.05 140 0.19 0.9084133 0.8592515
# Confidence Interval
# data entry
dat = ("
Pc N1 P1 N2 P2
95 76 0.05 78 0.17
99 113 0.05 115 0.22
95 101 0.05 100 0.21
99 143 0.05 140 0.19
")
df <- read.table(textConnection(dat),header=TRUE) # conversion to data frame
# df # optional display of entry data
# Vectors for results
LL1 <- vector() # lower limit 1 tail
UL1 <- vector() # upper limit 1 tail
LL2 <- vector() # lower limit 2 tail
UL2 <- vector() # upper limit 2 tail
# Calculations
for(i in 1 : nrow(df))
{
pc = df$Pc[i] # % confidence
prob = 1 - pc / 100 # alpha
n1 = df$N1[i]
p1 = df$P1[i]
n2 = df$N2[i]
p2 = df$P2[i]
diff = p1 - p2
se = sqrt(p1 * (1 - p1) / n1 + p2 * (1 - p2)/ n2)
za = abs(qnorm(prob / 1)) # 1 tail
LL1 <- append(LL1, diff - za * se)
UL1 <- append(UL1, diff + za * se)
za = abs(qnorm(prob / 2)) # 2 tail
LL2 <- append(LL2, diff - za * se)
UL2 <- append(UL2, diff + za * se)
}
df$LL1 <- LL1
df$UL1 <- UL1
df$LL2 <- LL2
df$UL2 <- UL2
df # Display entry data and result confidence intervals
The results are as follows
- Pc = % confidence
- N1, P1 = sample size and proportion of group 1
- N2, P2 = sample size and proportion of group 2
- LL1 and UL1 = lower and upper limits of confidence interval, 1 tail
- LL2 and UL2 = lower and upper limits of confidence interval, 2 tail
> df # Display entry data and result confidence intervals
Pc N1 P1 N2 P2 LL1 UL1 LL2 UL2
1 95 76 0.05 78 0.17 -0.2011494 -0.03885061 -0.2166955 -0.02330454
2 99 113 0.05 115 0.22 -0.2717369 -0.06826309 -0.2826473 -0.05735265
3 95 101 0.05 100 0.21 -0.2359006 -0.08409937 -0.2504412 -0.06955882
4 99 143 0.05 140 0.19 -0.2280163 -0.05198366 -0.2374554 -0.04254464
# Pilot study
# Parameters
pc = 95 # % confidence
p1 = 0.677 # proportion grp 1
p2 = 0.413 # proportion grp 2
intv = 5 # interval
maxN = 100 # maximum sample size
# vectors for results
SSiz <- vector() # sample size
CI1 <- vector() # confidence interval 1 tail
Diff1 <- vector() # difference in CI from previous row 1 tail
DecCase1 <- vector() # decrease in CI per case increase 1 tail
PDCase1 <- vector() # % decrease in CI per case increase 1 tailCI1 <- vector() # confidence interval 1 tail
CI2 <- vector() # confidence interval 2 tail
Diff2 <- vector() # difference in CI from previous row 2 tail
DecCase2 <- vector() # decrease in CI per case increase 2 tail
PDCase2 <- vector() # % decrease in CI per case increase 2 tail
# Calculations
prob = 1 - pc / 100
z1 = abs(qnorm(prob / 1)) # 1 tail
z2 = abs(qnorm(prob / 2)) # 2 tail
# first row
n = intv
SSiz <- append(SSiz,n)
se = sqrt(p1 * (1 - p1) / n + p2 * (1 - p2)/ n)
ci1 = z1 * se * 2
CI1 <- append(CI1,sprintf(ci1, fmt="%#.4f")) # confidence interval 1 tail
Diff1 <- append(Diff1,0) # difference in CI from previous row 1 tail
DecCase1 <- append(DecCase1,0) # decrease in CI per case increase 1 tail
PDCase1 <- append(PDCase1,0) # % decrease in CI per case increase 1 tailCI1 <- vector() # confidence interval 1 tail
ci2 = z2 * se * 2
CI2 <- append(CI2,sprintf(ci2, fmt="%#.4f")) # confidence interval 1 tail
Diff2 <- append(Diff2,0) # difference in CI from previous row 1 tail
DecCase2 <- append(DecCase2,0) # decrease in CI per case increase 1 tail
PDCase2 <- append(PDCase2,0) # % decrease in CI per case increase 1 tailCI1 <- vector() # confidence interval 1 tail
# subsequent rows
while(n < maxN)
{
n = n + intv
SSiz <- append(SSiz,n)
se = sqrt(p1 * (1 - p1) / n + p2 * (1 - p2)/ n)
oldci1 = ci1
ci1 = z1 * se * 2
CI1 <- append(CI1,sprintf(ci1, fmt="%#.4f")) # confidence interval 1 tail
diff1 = oldci1 - ci1
Diff1 <- append(Diff1,sprintf(diff1, fmt="%#.4f")) # difference in CI from previous row 1 tail
decCase1 = diff1 / intv
DecCase1 <- append(DecCase1,sprintf(decCase1, fmt="%#.4f")) # decrease in CI per case increase 1 tail
pDCase1 = sprintf(decCase1 / oldci1 * 100, fmt="%#.1f")
PDCase1 <- append(PDCase1,pDCase1) # % decrease in CI per case increase 1 tail
oldci2 = ci2
ci2 = z2 * se * 2
CI2 <- append(CI2,sprintf(ci2, fmt="%#.4f")) # confidence interval 2 tail
diff2 = oldci2 - ci2
Diff2 <- append(Diff2,sprintf(diff2, fmt="%#.4f")) # difference in CI from previous row 2 tail
decCase2 = diff2 / intv
DecCase2 <- append(DecCase2,sprintf(decCase2, fmt="%#.4f")) # decrease in CI per case increase 2 tail
pDCase2 = sprintf(decCase2 / oldci2 * 100, fmt="%#.1f")
PDCase2 <- append(PDCase2,pDCase2) # % decrease in CI per case increase 2 tail
}
df <- data.frame(SSiz,CI1,Diff1,DecCase1,PDCase1,CI2,Diff2,DecCase2,PDCase2)
df # display results in data frame
The results are as follows
- SSiz = sample size used (per group)
- CI1 and CI2 = confidence interval for that sample size (1 and 2 tail)
- Diff1 and Diff2 = difference in CI from previous row (1 and 2 tail)
- DecCase1 and DecCase2 = decrease in CI per case increase (1 and 2 tail)
- PDCase1 and PDCase2 = % decrease in CI per case increase, based on the previous row (1 and 2 tail)
> df # display results in data frame
SSiz CI1 Diff1 DecCase1 PDCase1 CI2 Diff2 DecCase2 PDCase2
1 5 0.9990 0 0 0 1.1904 0 0 0
2 10 0.7064 0.2926 0.0585 5.9 0.8417 0.3487 0.0697 5.9
3 15 0.5768 0.1296 0.0259 3.7 0.6873 0.1545 0.0309 3.7
4 20 0.4995 0.0773 0.0155 2.7 0.5952 0.0921 0.0184 2.7
5 25 0.4468 0.0527 0.0105 2.1 0.5324 0.0628 0.0126 2.1
6 30 0.4078 0.0389 0.0078 1.7 0.4860 0.0464 0.0093 1.7
7 35 0.3776 0.0303 0.0061 1.5 0.4499 0.0360 0.0072 1.5
8 40 0.3532 0.0244 0.0049 1.3 0.4209 0.0291 0.0058 1.3
9 45 0.3330 0.0202 0.0040 1.1 0.3968 0.0241 0.0048 1.1
10 50 0.3159 0.0171 0.0034 1.0 0.3764 0.0204 0.0041 1.0
11 55 0.3012 0.0147 0.0029 0.9 0.3589 0.0175 0.0035 0.9
12 60 0.2884 0.0128 0.0026 0.9 0.3436 0.0153 0.0031 0.9
13 65 0.2771 0.0113 0.0023 0.8 0.3302 0.0135 0.0027 0.8
14 70 0.2670 0.0101 0.0020 0.7 0.3181 0.0120 0.0024 0.7
15 75 0.2579 0.0091 0.0018 0.7 0.3074 0.0108 0.0022 0.7
16 80 0.2498 0.0082 0.0016 0.6 0.2976 0.0098 0.0020 0.6
17 85 0.2423 0.0075 0.0015 0.6 0.2887 0.0089 0.0018 0.6
18 90 0.2355 0.0068 0.0014 0.6 0.2806 0.0081 0.0016 0.6
19 95 0.2292 0.0063 0.0013 0.5 0.2731 0.0075 0.0015 0.5
20 100 0.2234 0.0058 0.0012 0.5 0.2662 0.0069 0.0014 0.5
Contents of E:34
Contents of F:35
Contents of G:36
introduction
Power=0.8
Power=0.9
Power=0.95
This subpanel presents tables of sample size (per group) comparing two unpaired proportions, for the following combinations.
- Powers ( 1 - β) of 0.8, 0.9, and 0.99
- Probability of Type I Error (α) of 0.1, 0.05, 0.01, and 0.001
- One and two tail models
- p1 and p2 are the proportions in the two groups to be detected
Sample size calculated to be less than 1/proportion (ssiz<1/p1 or ssiz<1/p2) are not included in the table, as a single positive case in the group will then exceed the proportion being compared.
Sample size for two proportions are used mostly in the Risk Difference model, although it is also used for Risk Ratio comparisons.
| α | 0.1 | 0.05 | 0.01 | 0.001 | α | 0.1 | 0.05 | 0.01 | 0.001 |
| p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail |
| 0.05 | 0.1 | 250 | 343 | 343 | 435 | 556 | 647 | 857 | 946 | 0.05 | 0.15 | 81 | 111 | 111 | 141 | 180 | 209 | 277 | 306 |
| 0.05 | 0.2 | 43 | 60 | 60 | 76 | 97 | 113 | 149 | 165 | 0.05 | 0.25 | 28 | 39 | 39 | 49 | 63 | 73 | 97 | 108 |
| 0.05 | 0.3 | 20 | 28 | 28 | 36 | 46 | 53 | 70 | 78 | 0.05 | 0.35 | | 21 | 21 | 27 | 35 | 41 | 54 | 59 |
| 0.05 | 0.4 | | | | 22 | 28 | 32 | 43 | 47 | 0.05 | 0.45 | | | | | 23 | 26 | 35 | 39 |
| 0.05 | 0.5 | | | | | | 22 | 29 | 32 | 0.05 | 0.55 | | | | | | | 25 | 27 |
| 0.05 | 0.6 | | | | | | | 21 | 23 | 0.05 | 0.65 | | | | | | | | 20 |
| 0.05 | 0.7 | | | | | | | | | 0.05 | 0.75 | | | | | | | | |
| 0.05 | 0.8 | | | | | | | | | 0.05 | 0.85 | | | | | | | | |
| 0.05 | 0.9 | | | | | | | | | 0.05 | 0.95 | | | | | | | | |
| 0.1 | 0.15 | 394 | 540 | 540 | 686 | 877 | 1021 | 1352 | 1493 | 0.1 | 0.2 | 115 | 157 | 157 | 199 | 255 | 297 | 393 | 434 |
| 0.1 | 0.25 | 57 | 79 | 79 | 100 | 128 | 149 | 197 | 218 | 0.1 | 0.3 | 36 | 49 | 49 | 62 | 79 | 92 | 122 | 135 |
| 0.1 | 0.35 | 25 | 34 | 34 | 43 | 55 | 64 | 85 | 94 | 0.1 | 0.4 | 18 | 25 | 25 | 32 | 41 | 48 | 63 | 70 |
| 0.1 | 0.45 | 14 | 20 | 20 | 25 | 32 | 37 | 49 | 54 | 0.1 | 0.5 | 11 | 16 | 16 | 20 | 25 | 30 | 39 | 44 |
| 0.1 | 0.55 | | 13 | 13 | 16 | 21 | 24 | 32 | 36 | 0.1 | 0.6 | | 11 | 11 | 14 | 17 | 20 | 27 | 30 |
| 0.1 | 0.65 | | | | 11 | 15 | 17 | 23 | 25 | 0.1 | 0.7 | | | | 10 | 12 | 15 | 19 | 21 |
| 0.1 | 0.75 | | | | | 11 | 12 | 17 | 18 | 0.1 | 0.8 | | | | | | 11 | 14 | 16 |
| 0.1 | 0.85 | | | | | | | 12 | 14 | 0.1 | 0.9 | | | | | | | 11 | 12 |
| 0.1 | 0.95 | | | | | | | | 10 | 0.15 | 0.2 | 520 | 714 | 714 | 906 | 1158 | 1348 | 1784 | 1971 |
| 0.15 | 0.25 | 144 | 197 | 197 | 250 | 320 | 373 | 494 | 545 | 0.15 | 0.3 | 69 | 95 | 95 | 121 | 155 | 180 | 238 | 263 |
| 0.15 | 0.35 | 42 | 57 | 57 | 73 | 93 | 109 | 144 | 159 | 0.15 | 0.4 | 28 | 39 | 39 | 49 | 63 | 74 | 97 | 108 |
| α | 0.1 | 0.05 | 0.01 | 0.001 | α | 0.1 | 0.05 | 0.01 | 0.001 |
| p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail |
| 0.15 | 0.45 | 21 | 28 | 28 | 36 | 46 | 54 | 71 | 78 | 0.15 | 0.5 | 16 | 22 | 22 | 27 | 35 | 41 | 54 | 60 |
| 0.15 | 0.55 | 12 | 17 | 17 | 22 | 28 | 32 | 43 | 47 | 0.15 | 0.6 | 10 | 14 | 14 | 17 | 22 | 26 | 35 | 38 |
| 0.15 | 0.65 | 8 | 11 | 11 | 14 | 18 | 21 | 28 | 31 | 0.15 | 0.7 | 7 | 9 | 9 | 12 | 15 | 18 | 24 | 26 |
| 0.15 | 0.75 | 6 | 8 | 8 | 10 | 13 | 15 | 20 | 22 | 0.15 | 0.8 | | 7 | 7 | 8 | 11 | 13 | 17 | 19 |
| 0.15 | 0.85 | | 6 | 6 | 7 | 9 | 11 | 14 | 16 | 0.15 | 0.9 | | | | 6 | 8 | 9 | 12 | 14 |
| 0.15 | 0.95 | | | | | 7 | 8 | 10 | 12 | 0.2 | 0.25 | 628 | 862 | 862 | 1094 | 1399 | 1628 | 2155 | 2381 |
| 0.2 | 0.3 | 169 | 231 | 231 | 294 | 376 | 437 | 579 | 639 | 0.2 | 0.35 | 79 | 109 | 109 | 138 | 177 | 206 | 273 | 301 |
| 0.2 | 0.4 | 47 | 64 | 64 | 82 | 105 | 122 | 161 | 178 | 0.2 | 0.45 | 31 | 43 | 43 | 54 | 70 | 81 | 107 | 119 |
| 0.2 | 0.5 | 22 | 31 | 31 | 39 | 50 | 58 | 77 | 85 | 0.2 | 0.55 | 17 | 23 | 23 | 29 | 38 | 44 | 58 | 64 |
| 0.2 | 0.6 | 13 | 18 | 18 | 23 | 29 | 34 | 45 | 50 | 0.2 | 0.65 | 10 | 14 | 14 | 18 | 23 | 27 | 36 | 40 |
| 0.2 | 0.7 | 8 | 12 | 12 | 15 | 19 | 22 | 29 | 32 | 0.2 | 0.75 | 7 | 10 | 10 | 12 | 16 | 18 | 24 | 27 |
| 0.2 | 0.8 | 6 | 8 | 8 | 10 | 13 | 15 | 20 | 22 | 0.2 | 0.85 | 5 | 7 | 7 | 8 | 11 | 13 | 17 | 19 |
| 0.2 | 0.9 | | 6 | 6 | 7 | 9 | 11 | 14 | 16 | 0.2 | 0.95 | | 5 | 5 | 6 | 8 | 9 | 12 | 13 |
| 0.25 | 0.3 | 719 | 986 | 986 | 1251 | 1600 | 1862 | 2465 | 2722 | 0.25 | 0.35 | 189 | 259 | 259 | 329 | 421 | 490 | 648 | 716 |
| 0.25 | 0.4 | 88 | 120 | 120 | 152 | 195 | 227 | 300 | 332 | 0.25 | 0.45 | 51 | 70 | 70 | 89 | 113 | 132 | 175 | 193 |
| 0.25 | 0.5 | 33 | 46 | 46 | 58 | 74 | 87 | 115 | 127 | 0.25 | 0.55 | 24 | 32 | 32 | 41 | 53 | 61 | 81 | 90 |
| 0.25 | 0.6 | 18 | 24 | 24 | 31 | 39 | 46 | 60 | 67 | 0.25 | 0.65 | 14 | 19 | 19 | 24 | 30 | 35 | 47 | 52 |
| 0.25 | 0.7 | 11 | 15 | 15 | 19 | 24 | 28 | 37 | 41 | 0.25 | 0.75 | 9 | 12 | 12 | 15 | 19 | 22 | 30 | 33 |
| 0.25 | 0.8 | 7 | 10 | 10 | 12 | 16 | 18 | 24 | 27 | 0.25 | 0.85 | 6 | 8 | 8 | 10 | 13 | 15 | 20 | 22 |
| 0.25 | 0.9 | 5 | 7 | 7 | 8 | 11 | 12 | 17 | 18 | 0.25 | 0.95 | 4 | 5 | 5 | 7 | 9 | 10 | 14 | 15 |
| 0.3 | 0.35 | 791 | 1084 | 1084 | 1377 | 1760 | 2049 | 2712 | 2995 | 0.3 | 0.4 | 205 | 281 | 281 | 356 | 456 | 530 | 702 | 776 |
| 0.3 | 0.45 | 94 | 128 | 128 | 163 | 208 | 242 | 321 | 354 | 0.3 | 0.5 | 54 | 74 | 74 | 93 | 120 | 139 | 184 | 204 |
| α | 0.1 | 0.05 | 0.01 | 0.001 | α | 0.1 | 0.05 | 0.01 | 0.001 |
| p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail |
| 0.3 | 0.55 | 35 | 48 | 48 | 61 | 78 | 90 | 120 | 132 | 0.3 | 0.6 | 24 | 33 | 33 | 42 | 54 | 63 | 84 | 93 |
| 0.3 | 0.65 | 18 | 25 | 25 | 31 | 40 | 47 | 62 | 68 | 0.3 | 0.7 | 14 | 19 | 19 | 24 | 30 | 36 | 47 | 52 |
| 0.3 | 0.75 | 11 | 15 | 15 | 19 | 24 | 28 | 37 | 41 | 0.3 | 0.8 | 8 | 12 | 12 | 15 | 19 | 22 | 29 | 32 |
| 0.3 | 0.85 | 7 | 9 | 9 | 12 | 15 | 18 | 24 | 26 | 0.3 | 0.9 | 6 | 8 | 8 | 10 | 12 | 15 | 19 | 21 |
| 0.3 | 0.95 | 5 | 6 | 6 | 8 | 10 | 12 | 16 | 17 | 0.35 | 0.4 | 845 | 1159 | 1159 | 1471 | 1881 | 2189 | 2897 | 3200 |
| 0.35 | 0.45 | 216 | 296 | 296 | 376 | 481 | 560 | 741 | 818 | 0.35 | 0.5 | 98 | 134 | 134 | 170 | 217 | 253 | 335 | 370 |
| 0.35 | 0.55 | 55 | 76 | 76 | 96 | 123 | 144 | 190 | 210 | 0.35 | 0.6 | 36 | 49 | 49 | 62 | 79 | 92 | 122 | 135 |
| 0.35 | 0.65 | 25 | 34 | 34 | 43 | 55 | 64 | 85 | 94 | 0.35 | 0.7 | 18 | 25 | 25 | 31 | 40 | 47 | 62 | 68 |
| 0.35 | 0.75 | 14 | 19 | 19 | 24 | 30 | 35 | 47 | 52 | 0.35 | 0.8 | 10 | 14 | 14 | 18 | 23 | 27 | 36 | 40 |
| 0.35 | 0.85 | 8 | 11 | 11 | 14 | 18 | 21 | 28 | 31 | 0.35 | 0.9 | 7 | 9 | 9 | 11 | 15 | 17 | 23 | 25 |
| 0.35 | 0.95 | 5 | 7 | 7 | 9 | 12 | 14 | 18 | 20 | 0.4 | 0.45 | 881 | 1208 | 1208 | 1534 | 1961 | 2282 | 3021 | 3337 |
| 0.4 | 0.5 | 223 | 305 | 305 | 388 | 496 | 577 | 764 | 844 | 0.4 | 0.55 | 100 | 136 | 136 | 173 | 222 | 258 | 342 | 377 |
| 0.4 | 0.6 | 56 | 77 | 77 | 97 | 125 | 145 | 192 | 212 | 0.4 | 0.65 | 36 | 49 | 49 | 62 | 79 | 92 | 122 | 135 |
| 0.4 | 0.7 | 24 | 33 | 33 | 42 | 54 | 63 | 84 | 93 | 0.4 | 0.75 | 18 | 24 | 24 | 31 | 39 | 46 | 60 | 67 |
| 0.4 | 0.8 | 13 | 18 | 18 | 23 | 29 | 34 | 45 | 50 | 0.4 | 0.85 | 10 | 14 | 14 | 17 | 22 | 26 | 35 | 38 |
| 0.4 | 0.9 | 8 | 11 | 11 | 14 | 17 | 20 | 27 | 30 | 0.4 | 0.95 | 6 | 8 | 8 | 11 | 14 | 16 | 21 | 23 |
| 0.45 | 0.5 | 899 | 1233 | 1233 | 1565 | 2001 | 2329 | 3083 | 3405 | 0.45 | 0.55 | 225 | 309 | 309 | 392 | 501 | 583 | 772 | 852 |
| 0.45 | 0.6 | 100 | 136 | 136 | 173 | 222 | 258 | 342 | 377 | 0.45 | 0.65 | 55 | 76 | 76 | 96 | 123 | 144 | 190 | 210 |
| 0.45 | 0.7 | 35 | 48 | 48 | 61 | 78 | 90 | 120 | 132 | 0.45 | 0.75 | 24 | 32 | 32 | 41 | 53 | 61 | 81 | 90 |
| 0.45 | 0.8 | 17 | 23 | 23 | 29 | 38 | 44 | 58 | 64 | 0.45 | 0.85 | 12 | 17 | 17 | 22 | 28 | 32 | 43 | 47 |
| 0.45 | 0.9 | 9 | 13 | 13 | 16 | 21 | 24 | 32 | 36 | 0.45 | 0.95 | 7 | 10 | 10 | 12 | 16 | 19 | 25 | 27 |
| 0.5 | 0.55 | 899 | 1233 | 1233 | 1565 | 2001 | 2329 | 3083 | 3405 | 0.5 | 0.6 | 223 | 305 | 305 | 388 | 496 | 577 | 764 | 844 |
| α | 0.1 | 0.05 | 0.01 | 0.001 | α | 0.1 | 0.05 | 0.01 | 0.001 |
| p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail |
| 0.5 | 0.65 | 98 | 134 | 134 | 170 | 217 | 253 | 335 | 370 | 0.5 | 0.7 | 54 | 74 | 74 | 93 | 120 | 139 | 184 | 204 |
| 0.5 | 0.75 | 33 | 46 | 46 | 58 | 74 | 87 | 115 | 127 | 0.5 | 0.8 | 22 | 31 | 31 | 39 | 50 | 58 | 77 | 85 |
| 0.5 | 0.85 | 16 | 22 | 22 | 27 | 35 | 41 | 54 | 60 | 0.5 | 0.9 | 11 | 16 | 16 | 20 | 25 | 30 | 39 | 44 |
| 0.5 | 0.95 | 8 | 12 | 12 | 15 | 19 | 22 | 29 | 32 | 0.55 | 0.6 | 881 | 1208 | 1208 | 1534 | 1961 | 2282 | 3021 | 3337 |
| 0.55 | 0.65 | 216 | 296 | 296 | 376 | 481 | 560 | 741 | 818 | 0.55 | 0.7 | 94 | 128 | 128 | 163 | 208 | 242 | 321 | 354 |
| 0.55 | 0.75 | 51 | 70 | 70 | 89 | 113 | 132 | 175 | 193 | 0.55 | 0.8 | 31 | 43 | 43 | 54 | 70 | 81 | 107 | 119 |
| 0.55 | 0.85 | 21 | 28 | 28 | 36 | 46 | 54 | 71 | 78 | 0.55 | 0.9 | 14 | 20 | 20 | 25 | 32 | 37 | 49 | 54 |
| 0.55 | 0.95 | 10 | 14 | 14 | 18 | 23 | 26 | 35 | 39 | 0.6 | 0.65 | 845 | 1159 | 1159 | 1471 | 1881 | 2189 | 2897 | 3200 |
| 0.6 | 0.7 | 205 | 281 | 281 | 356 | 456 | 530 | 702 | 776 | 0.6 | 0.75 | 88 | 120 | 120 | 152 | 195 | 227 | 300 | 332 |
| 0.6 | 0.8 | 47 | 64 | 64 | 82 | 105 | 122 | 161 | 178 | 0.6 | 0.85 | 28 | 39 | 39 | 49 | 63 | 74 | 97 | 108 |
| 0.6 | 0.9 | 18 | 25 | 25 | 32 | 41 | 48 | 63 | 70 | 0.6 | 0.95 | 12 | 17 | 17 | 22 | 28 | 32 | 43 | 47 |
| 0.65 | 0.7 | 791 | 1084 | 1084 | 1377 | 1760 | 2049 | 2712 | 2995 | 0.65 | 0.75 | 189 | 259 | 259 | 329 | 421 | 490 | 648 | 716 |
| 0.65 | 0.8 | 79 | 109 | 109 | 138 | 177 | 206 | 273 | 301 | 0.65 | 0.85 | 42 | 57 | 57 | 73 | 93 | 109 | 144 | 159 |
| 0.65 | 0.9 | 25 | 34 | 34 | 43 | 55 | 64 | 85 | 94 | 0.65 | 0.95 | 16 | 21 | 21 | 27 | 35 | 41 | 54 | 59 |
| 0.7 | 0.75 | 719 | 986 | 986 | 1251 | 1600 | 1862 | 2465 | 2722 | 0.7 | 0.8 | 169 | 231 | 231 | 294 | 376 | 437 | 579 | 639 |
| 0.7 | 0.85 | 69 | 95 | 95 | 121 | 155 | 180 | 238 | 263 | 0.7 | 0.9 | 36 | 49 | 49 | 62 | 79 | 92 | 122 | 135 |
| 0.7 | 0.95 | 20 | 28 | 28 | 36 | 46 | 53 | 70 | 78 | 0.75 | 0.8 | 628 | 862 | 862 | 1094 | 1399 | 1628 | 2155 | 2381 |
| 0.75 | 0.85 | 144 | 197 | 197 | 250 | 320 | 373 | 494 | 545 | 0.75 | 0.9 | 57 | 79 | 79 | 100 | 128 | 149 | 197 | 218 |
| 0.75 | 0.95 | 28 | 39 | 39 | 49 | 63 | 73 | 97 | 108 | 0.8 | 0.85 | 520 | 714 | 714 | 906 | 1158 | 1348 | 1784 | 1971 |
| 0.8 | 0.9 | 115 | 157 | 157 | 199 | 255 | 297 | 393 | 434 | 0.8 | 0.95 | 43 | 60 | 60 | 76 | 97 | 113 | 149 | 165 |
| 0.85 | 0.9 | 394 | 540 | 540 | 686 | 877 | 1021 | 1352 | 1493 | 0.85 | 0.95 | 81 | 111 | 111 | 141 | 180 | 209 | 277 | 306 |
| 0.9 | 0.95 | 250 | 343 | 343 | 435 | 556 | 647 | 857 | 946 |
| α | 0.1 | 0.05 | 0.01 | 0.001 | α | 0.1 | 0.05 | 0.01 | 0.001 |
| p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail |
| 0.05 | 0.1 | 363 | 474 | 474 | 582 | 721 | 824 | 1058 | 1158 | 0.05 | 0.15 | 117 | 153 | 153 | 188 | 232 | 266 | 342 | 374 |
| 0.05 | 0.2 | 63 | 82 | 82 | 101 | 125 | 143 | 183 | 201 | 0.05 | 0.25 | 41 | 53 | 53 | 65 | 81 | 93 | 119 | 131 |
| 0.05 | 0.3 | 29 | 38 | 38 | 47 | 58 | 67 | 86 | 94 | 0.05 | 0.35 | 22 | 29 | 29 | 36 | 44 | 51 | 66 | 72 |
| 0.05 | 0.4 | | 23 | 23 | 28 | 35 | 40 | 52 | 57 | 0.05 | 0.45 | | | | 23 | 29 | 33 | 42 | 46 |
| 0.05 | 0.5 | | | | | 24 | 27 | 35 | 39 | 0.05 | 0.55 | | | | | 20 | 23 | 30 | 33 |
| 0.05 | 0.6 | | | | | | | 25 | 28 | 0.05 | 0.65 | | | | | | | 22 | 24 |
| 0.05 | 0.7 | | | | | | | | 20 | 0.05 | 0.75 | | | | | | | | |
| 0.05 | 0.8 | | | | | | | | | 0.05 | 0.85 | | | | | | | | |
| 0.05 | 0.9 | | | | | | | | | 0.05 | 0.95 | | | | | | | | |
| 0.1 | 0.15 | 574 | 748 | 748 | 918 | 1137 | 1300 | 1670 | 1827 | 0.1 | 0.2 | 166 | 217 | 217 | 266 | 330 | 377 | 485 | 531 |
| 0.1 | 0.25 | 83 | 109 | 109 | 133 | 165 | 189 | 243 | 266 | 0.1 | 0.3 | 51 | 67 | 67 | 82 | 102 | 117 | 151 | 165 |
| 0.1 | 0.35 | 35 | 46 | 46 | 57 | 71 | 81 | 104 | 114 | 0.1 | 0.4 | 26 | 34 | 34 | 42 | 52 | 60 | 77 | 85 |
| 0.1 | 0.45 | 20 | 26 | 26 | 33 | 40 | 46 | 60 | 66 | 0.1 | 0.5 | 16 | 21 | 21 | 26 | 32 | 37 | 48 | 52 |
| 0.1 | 0.55 | 13 | 17 | 17 | 21 | 26 | 30 | 39 | 43 | 0.1 | 0.6 | 11 | 14 | 14 | 17 | 22 | 25 | 32 | 35 |
| 0.1 | 0.65 | | 12 | 12 | 15 | 18 | 21 | 27 | 30 | 0.1 | 0.7 | | 10 | 10 | 12 | 15 | 18 | 23 | 25 |
| 0.1 | 0.75 | | | | 10 | 13 | 15 | 20 | 21 | 0.1 | 0.8 | | | | | 11 | 13 | 17 | 18 |
| 0.1 | 0.85 | | | | | | 11 | 14 | 16 | 0.1 | 0.9 | | | | | | | 12 | 13 |
| 0.1 | 0.95 | | | | | | | 10 | 11 | 0.15 | 0.2 | 758 | 988 | 988 | 1212 | 1502 | 1717 | 2205 | 2412 |
| 0.15 | 0.25 | 209 | 273 | 273 | 335 | 415 | 474 | 609 | 666 | 0.15 | 0.3 | 101 | 131 | 131 | 161 | 200 | 229 | 294 | 322 |
| 0.15 | 0.35 | 60 | 79 | 79 | 97 | 120 | 137 | 177 | 194 | 0.15 | 0.4 | 41 | 53 | 53 | 65 | 81 | 93 | 120 | 131 |
| α | 0.1 | 0.05 | 0.01 | 0.001 | α | 0.1 | 0.05 | 0.01 | 0.001 |
| p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail |
| 0.15 | 0.45 | 29 | 39 | 39 | 47 | 59 | 67 | 87 | 95 | 0.15 | 0.5 | 22 | 29 | 29 | 36 | 45 | 51 | 66 | 72 |
| 0.15 | 0.55 | 17 | 23 | 23 | 28 | 35 | 40 | 52 | 57 | 0.15 | 0.6 | 14 | 18 | 18 | 23 | 28 | 32 | 42 | 46 |
| 0.15 | 0.65 | 11 | 15 | 15 | 19 | 23 | 26 | 34 | 38 | 0.15 | 0.7 | 9 | 12 | 12 | 15 | 19 | 22 | 28 | 31 |
| 0.15 | 0.75 | 8 | 10 | 10 | 13 | 16 | 18 | 24 | 26 | 0.15 | 0.8 | 7 | 9 | 9 | 11 | 13 | 15 | 20 | 22 |
| 0.15 | 0.85 | | 7 | 7 | 9 | 11 | 13 | 17 | 19 | 0.15 | 0.9 | | 6 | 6 | 7 | 9 | 11 | 14 | 16 |
| 0.15 | 0.95 | | | | 6 | 8 | 9 | 12 | 13 | 0.2 | 0.25 | 915 | 1193 | 1193 | 1464 | 1814 | 2074 | 2664 | 2914 |
| 0.2 | 0.3 | 245 | 320 | 320 | 392 | 486 | 556 | 714 | 781 | 0.2 | 0.35 | 115 | 150 | 150 | 185 | 229 | 262 | 336 | 368 |
| 0.2 | 0.4 | 68 | 89 | 89 | 109 | 135 | 154 | 198 | 217 | 0.2 | 0.45 | 45 | 59 | 59 | 72 | 90 | 102 | 132 | 144 |
| 0.2 | 0.5 | 32 | 42 | 42 | 52 | 64 | 73 | 94 | 103 | 0.2 | 0.55 | 24 | 31 | 31 | 39 | 48 | 55 | 71 | 77 |
| 0.2 | 0.6 | 19 | 24 | 24 | 30 | 37 | 43 | 55 | 60 | 0.2 | 0.65 | 15 | 19 | 19 | 24 | 30 | 34 | 44 | 48 |
| 0.2 | 0.7 | 12 | 16 | 16 | 19 | 24 | 27 | 35 | 39 | 0.2 | 0.75 | 10 | 13 | 13 | 16 | 20 | 22 | 29 | 32 |
| 0.2 | 0.8 | 8 | 10 | 10 | 13 | 16 | 19 | 24 | 26 | 0.2 | 0.85 | 7 | 9 | 9 | 11 | 13 | 15 | 20 | 22 |
| 0.2 | 0.9 | 5 | 7 | 7 | 9 | 11 | 13 | 17 | 18 | 0.2 | 0.95 | | 6 | 6 | 7 | 9 | 11 | 14 | 15 |
| 0.25 | 0.3 | 1047 | 1365 | 1365 | 1674 | 2074 | 2371 | 3046 | 3332 | 0.25 | 0.35 | 275 | 358 | 358 | 440 | 545 | 623 | 800 | 876 |
| 0.25 | 0.4 | 127 | 166 | 166 | 203 | 252 | 288 | 370 | 405 | 0.25 | 0.45 | 74 | 96 | 96 | 118 | 146 | 167 | 215 | 235 |
| 0.25 | 0.5 | 48 | 63 | 63 | 77 | 96 | 110 | 141 | 154 | 0.25 | 0.55 | 34 | 44 | 44 | 54 | 68 | 77 | 100 | 109 |
| 0.25 | 0.6 | 25 | 33 | 33 | 40 | 50 | 57 | 74 | 81 | 0.25 | 0.65 | 19 | 25 | 25 | 31 | 38 | 44 | 57 | 62 |
| 0.25 | 0.7 | 15 | 20 | 20 | 24 | 30 | 35 | 45 | 49 | 0.25 | 0.75 | 12 | 16 | 16 | 19 | 24 | 28 | 36 | 39 |
| 0.25 | 0.8 | 10 | 13 | 13 | 16 | 20 | 22 | 29 | 32 | 0.25 | 0.85 | 8 | 10 | 10 | 13 | 16 | 18 | 24 | 26 |
| 0.25 | 0.9 | 6 | 8 | 8 | 10 | 13 | 15 | 20 | 21 | 0.25 | 0.95 | 5 | 7 | 7 | 8 | 11 | 12 | 16 | 18 |
| 0.3 | 0.35 | 1152 | 1502 | 1502 | 1842 | 2283 | 2609 | 3352 | 3666 | 0.3 | 0.4 | 298 | 388 | 388 | 477 | 590 | 675 | 867 | 949 |
| 0.3 | 0.45 | 136 | 177 | 177 | 217 | 269 | 308 | 396 | 433 | 0.3 | 0.5 | 78 | 101 | 101 | 124 | 154 | 177 | 227 | 248 |
| α | 0.1 | 0.05 | 0.01 | 0.001 | α | 0.1 | 0.05 | 0.01 | 0.001 |
| p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail |
| 0.3 | 0.55 | 50 | 66 | 66 | 81 | 100 | 114 | 147 | 161 | 0.3 | 0.6 | 35 | 46 | 46 | 56 | 70 | 80 | 103 | 112 |
| 0.3 | 0.65 | 26 | 33 | 33 | 41 | 51 | 59 | 75 | 83 | 0.3 | 0.7 | 19 | 25 | 25 | 31 | 39 | 44 | 57 | 63 |
| 0.3 | 0.75 | 15 | 20 | 20 | 24 | 30 | 35 | 45 | 49 | 0.3 | 0.8 | 12 | 16 | 16 | 19 | 24 | 27 | 35 | 39 |
| 0.3 | 0.85 | 9 | 12 | 12 | 15 | 19 | 22 | 28 | 31 | 0.3 | 0.9 | 8 | 10 | 10 | 12 | 15 | 18 | 23 | 25 |
| 0.3 | 0.95 | 6 | 8 | 8 | 10 | 12 | 14 | 19 | 20 | 0.35 | 0.4 | 1231 | 1604 | 1604 | 1969 | 2439 | 2788 | 3581 | 3917 |
| 0.35 | 0.45 | 314 | 410 | 410 | 503 | 623 | 712 | 915 | 1001 | 0.35 | 0.5 | 142 | 185 | 185 | 227 | 281 | 321 | 413 | 452 |
| 0.35 | 0.55 | 80 | 105 | 105 | 128 | 159 | 182 | 234 | 256 | 0.35 | 0.6 | 51 | 67 | 67 | 82 | 102 | 117 | 150 | 164 |
| 0.35 | 0.65 | 35 | 46 | 46 | 57 | 70 | 81 | 104 | 114 | 0.35 | 0.7 | 26 | 33 | 33 | 41 | 51 | 59 | 75 | 83 |
| 0.35 | 0.75 | 19 | 25 | 25 | 31 | 38 | 44 | 57 | 62 | 0.35 | 0.8 | 15 | 19 | 19 | 24 | 30 | 34 | 44 | 48 |
| 0.35 | 0.85 | 11 | 15 | 15 | 19 | 23 | 26 | 34 | 38 | 0.35 | 0.9 | 9 | 12 | 12 | 15 | 18 | 21 | 27 | 30 |
| 0.35 | 0.95 | 7 | 9 | 9 | 12 | 14 | 17 | 22 | 24 | 0.4 | 0.45 | 1283 | 1673 | 1673 | 2053 | 2543 | 2907 | 3734 | 4084 |
| 0.4 | 0.5 | 324 | 423 | 423 | 519 | 643 | 735 | 944 | 1032 | 0.4 | 0.55 | 144 | 188 | 188 | 231 | 287 | 328 | 421 | 461 |
| 0.4 | 0.6 | 81 | 106 | 106 | 130 | 161 | 184 | 237 | 259 | 0.4 | 0.65 | 51 | 67 | 67 | 82 | 102 | 117 | 150 | 164 |
| 0.4 | 0.7 | 35 | 46 | 46 | 56 | 70 | 80 | 103 | 112 | 0.4 | 0.75 | 25 | 33 | 33 | 40 | 50 | 57 | 74 | 81 |
| 0.4 | 0.8 | 19 | 24 | 24 | 30 | 37 | 43 | 55 | 60 | 0.4 | 0.85 | 14 | 18 | 18 | 23 | 28 | 32 | 42 | 46 |
| 0.4 | 0.9 | 11 | 14 | 14 | 17 | 22 | 25 | 32 | 35 | 0.4 | 0.95 | 8 | 11 | 11 | 14 | 17 | 19 | 25 | 28 |
| 0.45 | 0.5 | 1309 | 1707 | 1707 | 2095 | 2595 | 2966 | 3811 | 4168 | 0.45 | 0.55 | 327 | 427 | 427 | 524 | 649 | 742 | 953 | 1043 |
| 0.45 | 0.6 | 144 | 188 | 188 | 231 | 287 | 328 | 421 | 461 | 0.45 | 0.65 | 80 | 105 | 105 | 128 | 159 | 182 | 234 | 256 |
| 0.45 | 0.7 | 50 | 66 | 66 | 81 | 100 | 114 | 147 | 161 | 0.45 | 0.75 | 34 | 44 | 44 | 54 | 68 | 77 | 100 | 109 |
| 0.45 | 0.8 | 24 | 31 | 31 | 39 | 48 | 55 | 71 | 77 | 0.45 | 0.85 | 17 | 23 | 23 | 28 | 35 | 40 | 52 | 57 |
| 0.45 | 0.9 | 13 | 17 | 17 | 21 | 26 | 30 | 39 | 43 | 0.45 | 0.95 | 10 | 13 | 13 | 16 | 20 | 23 | 30 | 33 |
| 0.5 | 0.55 | 1309 | 1707 | 1707 | 2095 | 2595 | 2966 | 3811 | 4168 | 0.5 | 0.6 | 324 | 423 | 423 | 519 | 643 | 735 | 944 | 1032 |
| α | 0.1 | 0.05 | 0.01 | 0.001 | α | 0.1 | 0.05 | 0.01 | 0.001 |
| p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail |
| 0.5 | 0.65 | 142 | 185 | 185 | 227 | 281 | 321 | 413 | 452 | 0.5 | 0.7 | 78 | 101 | 101 | 124 | 154 | 177 | 227 | 248 |
| 0.5 | 0.75 | 48 | 63 | 63 | 77 | 96 | 110 | 141 | 154 | 0.5 | 0.8 | 32 | 42 | 42 | 52 | 64 | 73 | 94 | 103 |
| 0.5 | 0.85 | 22 | 29 | 29 | 36 | 45 | 51 | 66 | 72 | 0.5 | 0.9 | 16 | 21 | 21 | 26 | 32 | 37 | 48 | 52 |
| 0.5 | 0.95 | 12 | 15 | 15 | 19 | 24 | 27 | 35 | 39 | 0.55 | 0.6 | 1283 | 1673 | 1673 | 2053 | 2543 | 2907 | 3734 | 4084 |
| 0.55 | 0.65 | 314 | 410 | 410 | 503 | 623 | 712 | 915 | 1001 | 0.55 | 0.7 | 136 | 177 | 177 | 217 | 269 | 308 | 396 | 433 |
| 0.55 | 0.75 | 74 | 96 | 96 | 118 | 146 | 167 | 215 | 235 | 0.55 | 0.8 | 45 | 59 | 59 | 72 | 90 | 102 | 132 | 144 |
| 0.55 | 0.85 | 29 | 39 | 39 | 47 | 59 | 67 | 87 | 95 | 0.55 | 0.9 | 20 | 26 | 26 | 33 | 40 | 46 | 60 | 66 |
| 0.55 | 0.95 | 14 | 19 | 19 | 23 | 29 | 33 | 42 | 46 | 0.6 | 0.65 | 1231 | 1604 | 1604 | 1969 | 2439 | 2788 | 3581 | 3917 |
| 0.6 | 0.7 | 298 | 388 | 388 | 477 | 590 | 675 | 867 | 949 | 0.6 | 0.75 | 127 | 166 | 166 | 203 | 252 | 288 | 370 | 405 |
| 0.6 | 0.8 | 68 | 89 | 89 | 109 | 135 | 154 | 198 | 217 | 0.6 | 0.85 | 41 | 53 | 53 | 65 | 81 | 93 | 120 | 131 |
| 0.6 | 0.9 | 26 | 34 | 34 | 42 | 52 | 60 | 77 | 85 | 0.6 | 0.95 | 18 | 23 | 23 | 28 | 35 | 40 | 52 | 57 |
| 0.65 | 0.7 | 1152 | 1502 | 1502 | 1842 | 2283 | 2609 | 3352 | 3666 | 0.65 | 0.75 | 275 | 358 | 358 | 440 | 545 | 623 | 800 | 876 |
| 0.65 | 0.8 | 115 | 150 | 150 | 185 | 229 | 262 | 336 | 368 | 0.65 | 0.85 | 60 | 79 | 79 | 97 | 120 | 137 | 177 | 194 |
| 0.65 | 0.9 | 35 | 46 | 46 | 57 | 71 | 81 | 104 | 114 | 0.65 | 0.95 | 22 | 29 | 29 | 36 | 44 | 51 | 66 | 72 |
| 0.7 | 0.75 | 1047 | 1365 | 1365 | 1674 | 2074 | 2371 | 3046 | 3332 | 0.7 | 0.8 | 245 | 320 | 320 | 392 | 486 | 556 | 714 | 781 |
| 0.7 | 0.85 | 101 | 131 | 131 | 161 | 200 | 229 | 294 | 322 | 0.7 | 0.9 | 51 | 67 | 67 | 82 | 102 | 117 | 151 | 165 |
| 0.7 | 0.95 | 29 | 38 | 38 | 47 | 58 | 67 | 86 | 94 | 0.75 | 0.8 | 915 | 1193 | 1193 | 1464 | 1814 | 2074 | 2664 | 2914 |
| 0.75 | 0.85 | 209 | 273 | 273 | 335 | 415 | 474 | 609 | 666 | 0.75 | 0.9 | 83 | 109 | 109 | 133 | 165 | 189 | 243 | 266 |
| 0.75 | 0.95 | 41 | 53 | 53 | 65 | 81 | 93 | 119 | 131 | 0.8 | 0.85 | 758 | 988 | 988 | 1212 | 1502 | 1717 | 2205 | 2412 |
| 0.8 | 0.9 | 166 | 217 | 217 | 266 | 330 | 377 | 485 | 531 | 0.8 | 0.95 | 63 | 82 | 82 | 101 | 125 | 143 | 183 | 201 |
| 0.85 | 0.9 | 574 | 748 | 748 | 918 | 1137 | 1300 | 1670 | 1827 | 0.85 | 0.95 | 117 | 153 | 153 | 188 | 232 | 266 | 342 | 374 |
| 0.9 | 0.95 | 363 | 474 | 474 | 582 | 721 | 824 | 1058 | 1158 |
| α | 0.1 | 0.05 | 0.01 | 0.001 | α | 0.1 | 0.05 | 0.01 | 0.001 |
| p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail |
| 0.05 | 0.1 | 473 | 598 | 598 | 719 | 872 | 986 | 1241 | 1348 | 0.05 | 0.15 | 152 | 193 | 193 | 231 | 281 | 318 | 400 | 435 |
| 0.05 | 0.2 | 81 | 103 | 103 | 124 | 151 | 170 | 215 | 233 | 0.05 | 0.25 | 53 | 67 | 67 | 80 | 98 | 111 | 139 | 152 |
| 0.05 | 0.3 | 38 | 48 | 48 | 58 | 70 | 79 | 100 | 109 | 0.05 | 0.35 | 29 | 36 | 36 | 44 | 53 | 60 | 76 | 83 |
| 0.05 | 0.4 | 22 | 29 | 29 | 34 | 42 | 48 | 60 | 66 | 0.05 | 0.45 | | 23 | 23 | 28 | 34 | 39 | 49 | 53 |
| 0.05 | 0.5 | | | | 23 | 28 | 32 | 41 | 44 | 0.05 | 0.55 | | | | | 24 | 27 | 34 | 37 |
| 0.05 | 0.6 | | | | | 20 | 23 | 29 | 31 | 0.05 | 0.65 | | | | | | | 25 | 27 |
| 0.05 | 0.7 | | | | | | | 21 | 23 | 0.05 | 0.75 | | | | | | | | 20 |
| 0.05 | 0.8 | | | | | | | | | 0.05 | 0.85 | | | | | | | | |
| 0.05 | 0.9 | | | | | | | | | 0.05 | 0.95 | | | | | | | | |
| 0.1 | 0.15 | 747 | 945 | 945 | 1135 | 1377 | 1556 | 1958 | 2128 | 0.1 | 0.2 | 216 | 274 | 274 | 329 | 399 | 451 | 568 | 618 |
| 0.1 | 0.25 | 108 | 137 | 137 | 164 | 200 | 226 | 284 | 309 | 0.1 | 0.3 | 67 | 84 | 84 | 101 | 123 | 140 | 176 | 191 |
| 0.1 | 0.35 | 46 | 58 | 58 | 70 | 85 | 96 | 122 | 132 | 0.1 | 0.4 | 34 | 43 | 43 | 52 | 63 | 71 | 90 | 98 |
| 0.1 | 0.45 | 26 | 33 | 33 | 40 | 48 | 55 | 69 | 76 | 0.1 | 0.5 | 21 | 26 | 26 | 32 | 39 | 44 | 55 | 60 |
| 0.1 | 0.55 | 17 | 21 | 21 | 26 | 31 | 35 | 45 | 49 | 0.1 | 0.6 | 14 | 17 | 17 | 21 | 26 | 29 | 37 | 41 |
| 0.1 | 0.65 | 11 | 14 | 14 | 18 | 21 | 24 | 31 | 34 | 0.1 | 0.7 | | 12 | 12 | 15 | 18 | 21 | 26 | 29 |
| 0.1 | 0.75 | | 10 | 10 | 12 | 15 | 17 | 22 | 24 | 0.1 | 0.8 | | | | 10 | 13 | 15 | 19 | 21 |
| 0.1 | 0.85 | | | | | 11 | 12 | 16 | 17 | 0.1 | 0.9 | | | | | | 10 | 13 | 15 |
| 0.1 | 0.95 | | | | | | | 11 | 12 | 0.15 | 0.2 | 987 | 1248 | 1248 | 1498 | 1819 | 2055 | 2586 | 2810 |
| 0.15 | 0.25 | 272 | 344 | 344 | 413 | 502 | 567 | 714 | 776 | 0.15 | 0.3 | 131 | 166 | 166 | 199 | 242 | 273 | 344 | 374 |
| 0.15 | 0.35 | 78 | 99 | 99 | 119 | 145 | 164 | 207 | 225 | 0.15 | 0.4 | 53 | 67 | 67 | 80 | 98 | 111 | 140 | 152 |
| α | 0.1 | 0.05 | 0.01 | 0.001 | α | 0.1 | 0.05 | 0.01 | 0.001 |
| p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail |
| 0.15 | 0.45 | 38 | 48 | 48 | 58 | 71 | 80 | 101 | 110 | 0.15 | 0.5 | 29 | 37 | 37 | 44 | 54 | 61 | 77 | 84 |
| 0.15 | 0.55 | 22 | 29 | 29 | 34 | 42 | 48 | 60 | 66 | 0.15 | 0.6 | 18 | 23 | 23 | 28 | 34 | 38 | 48 | 53 |
| 0.15 | 0.65 | 14 | 18 | 18 | 22 | 27 | 31 | 39 | 43 | 0.15 | 0.7 | 12 | 15 | 15 | 18 | 23 | 26 | 33 | 36 |
| 0.15 | 0.75 | 10 | 13 | 13 | 15 | 19 | 21 | 27 | 30 | 0.15 | 0.8 | 8 | 10 | 10 | 13 | 16 | 18 | 23 | 25 |
| 0.15 | 0.85 | 7 | 9 | 9 | 11 | 13 | 15 | 19 | 21 | 0.15 | 0.9 | | 7 | 7 | 9 | 11 | 12 | 16 | 17 |
| 0.15 | 0.95 | | 6 | 6 | 7 | 9 | 10 | 13 | 15 | 0.2 | 0.25 | 1193 | 1507 | 1507 | 1810 | 2197 | 2482 | 3124 | 3394 |
| 0.2 | 0.3 | 319 | 404 | 404 | 485 | 589 | 665 | 837 | 910 | 0.2 | 0.35 | 150 | 190 | 190 | 228 | 277 | 313 | 394 | 428 |
| 0.2 | 0.4 | 88 | 111 | 111 | 134 | 163 | 184 | 232 | 252 | 0.2 | 0.45 | 58 | 74 | 74 | 89 | 108 | 122 | 154 | 167 |
| 0.2 | 0.5 | 41 | 52 | 52 | 63 | 77 | 87 | 110 | 120 | 0.2 | 0.55 | 31 | 39 | 39 | 47 | 58 | 65 | 82 | 90 |
| 0.2 | 0.6 | 24 | 30 | 30 | 36 | 44 | 50 | 64 | 69 | 0.2 | 0.65 | 19 | 24 | 24 | 29 | 35 | 40 | 51 | 55 |
| 0.2 | 0.7 | 15 | 19 | 19 | 23 | 28 | 32 | 41 | 44 | 0.2 | 0.75 | 12 | 16 | 16 | 19 | 23 | 26 | 33 | 36 |
| 0.2 | 0.8 | 10 | 13 | 13 | 15 | 19 | 22 | 27 | 30 | 0.2 | 0.85 | 8 | 10 | 10 | 13 | 16 | 18 | 23 | 25 |
| 0.2 | 0.9 | 7 | 8 | 8 | 10 | 13 | 15 | 19 | 21 | 0.2 | 0.95 | 5 | 7 | 7 | 8 | 11 | 12 | 16 | 17 |
| 0.25 | 0.3 | 1364 | 1724 | 1724 | 2070 | 2513 | 2838 | 3573 | 3882 | 0.25 | 0.35 | 358 | 452 | 452 | 543 | 660 | 745 | 938 | 1019 |
| 0.25 | 0.4 | 165 | 209 | 209 | 251 | 305 | 344 | 434 | 471 | 0.25 | 0.45 | 95 | 121 | 121 | 145 | 177 | 200 | 252 | 273 |
| 0.25 | 0.5 | 62 | 79 | 79 | 95 | 115 | 131 | 165 | 179 | 0.25 | 0.55 | 44 | 55 | 55 | 67 | 81 | 92 | 116 | 126 |
| 0.25 | 0.6 | 32 | 41 | 41 | 49 | 60 | 68 | 86 | 94 | 0.25 | 0.65 | 25 | 31 | 31 | 38 | 46 | 52 | 66 | 72 |
| 0.25 | 0.7 | 19 | 24 | 24 | 29 | 36 | 41 | 52 | 56 | 0.25 | 0.75 | 15 | 19 | 19 | 23 | 29 | 33 | 41 | 45 |
| 0.25 | 0.8 | 12 | 16 | 16 | 19 | 23 | 26 | 33 | 36 | 0.25 | 0.85 | 10 | 13 | 13 | 15 | 19 | 21 | 27 | 30 |
| 0.25 | 0.9 | 8 | 10 | 10 | 12 | 15 | 17 | 22 | 24 | 0.25 | 0.95 | 6 | 8 | 8 | 10 | 12 | 14 | 18 | 20 |
| 0.3 | 0.35 | 1501 | 1897 | 1897 | 2278 | 2765 | 3123 | 3931 | 4271 | 0.3 | 0.4 | 388 | 490 | 490 | 589 | 715 | 808 | 1017 | 1105 |
| 0.3 | 0.45 | 177 | 223 | 223 | 268 | 326 | 368 | 464 | 504 | 0.3 | 0.5 | 101 | 128 | 128 | 153 | 186 | 211 | 266 | 289 |
| α | 0.1 | 0.05 | 0.01 | 0.001 | α | 0.1 | 0.05 | 0.01 | 0.001 |
| p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail |
| 0.3 | 0.55 | 65 | 82 | 82 | 99 | 121 | 136 | 172 | 187 | 0.3 | 0.6 | 45 | 57 | 57 | 69 | 84 | 95 | 120 | 130 |
| 0.3 | 0.65 | 33 | 42 | 42 | 50 | 61 | 69 | 88 | 96 | 0.3 | 0.7 | 25 | 32 | 32 | 38 | 46 | 53 | 67 | 72 |
| 0.3 | 0.75 | 19 | 24 | 24 | 29 | 36 | 41 | 52 | 56 | 0.3 | 0.8 | 15 | 19 | 19 | 23 | 28 | 32 | 41 | 44 |
| 0.3 | 0.85 | 12 | 15 | 15 | 18 | 23 | 26 | 33 | 36 | 0.3 | 0.9 | 9 | 12 | 12 | 15 | 18 | 21 | 26 | 29 |
| 0.3 | 0.95 | 7 | 10 | 10 | 12 | 14 | 16 | 21 | 23 | 0.35 | 0.4 | 1604 | 2027 | 2027 | 2434 | 2954 | 3337 | 4201 | 4564 |
| 0.35 | 0.45 | 409 | 517 | 517 | 621 | 754 | 852 | 1073 | 1166 | 0.35 | 0.5 | 184 | 233 | 233 | 280 | 340 | 384 | 484 | 526 |
| 0.35 | 0.55 | 104 | 132 | 132 | 158 | 192 | 217 | 274 | 298 | 0.35 | 0.6 | 66 | 84 | 84 | 101 | 123 | 139 | 175 | 191 |
| 0.35 | 0.65 | 46 | 58 | 58 | 70 | 85 | 96 | 121 | 132 | 0.35 | 0.7 | 33 | 42 | 42 | 50 | 61 | 69 | 88 | 96 |
| 0.35 | 0.75 | 25 | 31 | 31 | 38 | 46 | 52 | 66 | 72 | 0.35 | 0.8 | 19 | 24 | 24 | 29 | 35 | 40 | 51 | 55 |
| 0.35 | 0.85 | 14 | 18 | 18 | 22 | 27 | 31 | 39 | 43 | 0.35 | 0.9 | 11 | 14 | 14 | 18 | 21 | 24 | 31 | 34 |
| 0.35 | 0.95 | 9 | 11 | 11 | 14 | 17 | 19 | 25 | 27 | 0.4 | 0.45 | 1672 | 2114 | 2114 | 2538 | 3080 | 3480 | 4380 | 4758 |
| 0.4 | 0.5 | 422 | 533 | 533 | 641 | 778 | 879 | 1106 | 1202 | 0.4 | 0.55 | 188 | 238 | 238 | 286 | 347 | 392 | 494 | 536 |
| 0.4 | 0.6 | 105 | 133 | 133 | 160 | 194 | 220 | 277 | 301 | 0.4 | 0.65 | 66 | 84 | 84 | 101 | 123 | 139 | 175 | 191 |
| 0.4 | 0.7 | 45 | 57 | 57 | 69 | 84 | 95 | 120 | 130 | 0.4 | 0.75 | 32 | 41 | 41 | 49 | 60 | 68 | 86 | 94 |
| 0.4 | 0.8 | 24 | 30 | 30 | 36 | 44 | 50 | 64 | 69 | 0.4 | 0.85 | 18 | 23 | 23 | 28 | 34 | 38 | 48 | 53 |
| 0.4 | 0.9 | 14 | 17 | 17 | 21 | 26 | 29 | 37 | 41 | 0.4 | 0.95 | 10 | 13 | 13 | 16 | 20 | 23 | 29 | 31 |
| 0.45 | 0.5 | 1707 | 2157 | 2157 | 2590 | 3143 | 3551 | 4470 | 4856 | 0.45 | 0.55 | 426 | 539 | 539 | 647 | 786 | 888 | 1118 | 1214 |
| 0.45 | 0.6 | 188 | 238 | 238 | 286 | 347 | 392 | 494 | 536 | 0.45 | 0.65 | 104 | 132 | 132 | 158 | 192 | 217 | 274 | 298 |
| 0.45 | 0.7 | 65 | 82 | 82 | 99 | 121 | 136 | 172 | 187 | 0.45 | 0.75 | 44 | 55 | 55 | 67 | 81 | 92 | 116 | 126 |
| 0.45 | 0.8 | 31 | 39 | 39 | 47 | 58 | 65 | 82 | 90 | 0.45 | 0.85 | 22 | 29 | 29 | 34 | 42 | 48 | 60 | 66 |
| 0.45 | 0.9 | 17 | 21 | 21 | 26 | 31 | 35 | 45 | 49 | 0.45 | 0.95 | 12 | 16 | 16 | 19 | 24 | 27 | 34 | 37 |
| 0.5 | 0.55 | 1707 | 2157 | 2157 | 2590 | 3143 | 3551 | 4470 | 4856 | 0.5 | 0.6 | 422 | 533 | 533 | 641 | 778 | 879 | 1106 | 1202 |
| α | 0.1 | 0.05 | 0.01 | 0.001 | α | 0.1 | 0.05 | 0.01 | 0.001 |
| p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | p1 | p2 | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail | 1 tail | 2 tail |
| 0.5 | 0.65 | 184 | 233 | 233 | 280 | 340 | 384 | 484 | 526 | 0.5 | 0.7 | 101 | 128 | 128 | 153 | 186 | 211 | 266 | 289 |
| 0.5 | 0.75 | 62 | 79 | 79 | 95 | 115 | 131 | 165 | 179 | 0.5 | 0.8 | 41 | 52 | 52 | 63 | 77 | 87 | 110 | 120 |
| 0.5 | 0.85 | 29 | 37 | 37 | 44 | 54 | 61 | 77 | 84 | 0.5 | 0.9 | 21 | 26 | 26 | 32 | 39 | 44 | 55 | 60 |
| 0.5 | 0.95 | 15 | 19 | 19 | 23 | 28 | 32 | 41 | 44 | 0.55 | 0.6 | 1672 | 2114 | 2114 | 2538 | 3080 | 3480 | 4380 | 4758 |
| 0.55 | 0.65 | 409 | 517 | 517 | 621 | 754 | 852 | 1073 | 1166 | 0.55 | 0.7 | 177 | 223 | 223 | 268 | 326 | 368 | 464 | 504 |
| 0.55 | 0.75 | 95 | 121 | 121 | 145 | 177 | 200 | 252 | 273 | 0.55 | 0.8 | 58 | 74 | 74 | 89 | 108 | 122 | 154 | 167 |
| 0.55 | 0.85 | 38 | 48 | 48 | 58 | 71 | 80 | 101 | 110 | 0.55 | 0.9 | 26 | 33 | 33 | 40 | 48 | 55 | 69 | 76 |
| 0.55 | 0.95 | 18 | 23 | 23 | 28 | 34 | 39 | 49 | 53 | 0.6 | 0.65 | 1604 | 2027 | 2027 | 2434 | 2954 | 3337 | 4201 | 4564 |
| 0.6 | 0.7 | 388 | 490 | 490 | 589 | 715 | 808 | 1017 | 1105 | 0.6 | 0.75 | 165 | 209 | 209 | 251 | 305 | 344 | 434 | 471 |
| 0.6 | 0.8 | 88 | 111 | 111 | 134 | 163 | 184 | 232 | 252 | 0.6 | 0.85 | 53 | 67 | 67 | 80 | 98 | 111 | 140 | 152 |
| 0.6 | 0.9 | 34 | 43 | 43 | 52 | 63 | 71 | 90 | 98 | 0.6 | 0.95 | 22 | 29 | 29 | 34 | 42 | 48 | 60 | 66 |
| 0.65 | 0.7 | 1501 | 1897 | 1897 | 2278 | 2765 | 3123 | 3931 | 4271 | 0.65 | 0.75 | 358 | 452 | 452 | 543 | 660 | 745 | 938 | 1019 |
| 0.65 | 0.8 | 150 | 190 | 190 | 228 | 277 | 313 | 394 | 428 | 0.65 | 0.85 | 78 | 99 | 99 | 119 | 145 | 164 | 207 | 225 |
| 0.65 | 0.9 | 46 | 58 | 58 | 70 | 85 | 96 | 122 | 132 | 0.65 | 0.95 | 29 | 36 | 36 | 44 | 53 | 60 | 76 | 83 |
| 0.7 | 0.75 | 1364 | 1724 | 1724 | 2070 | 2513 | 2838 | 3573 | 3882 | 0.7 | 0.8 | 319 | 404 | 404 | 485 | 589 | 665 | 837 | 910 |
| 0.7 | 0.85 | 131 | 166 | 166 | 199 | 242 | 273 | 344 | 374 | 0.7 | 0.9 | 67 | 84 | 84 | 101 | 123 | 140 | 176 | 191 |
| 0.7 | 0.95 | 38 | 48 | 48 | 58 | 70 | 79 | 100 | 109 | 0.75 | 0.8 | 1193 | 1507 | 1507 | 1810 | 2197 | 2482 | 3124 | 3394 |
| 0.75 | 0.85 | 272 | 344 | 344 | 413 | 502 | 567 | 714 | 776 | 0.75 | 0.9 | 108 | 137 | 137 | 164 | 200 | 226 | 284 | 309 |
| 0.75 | 0.95 | 53 | 67 | 67 | 80 | 98 | 111 | 139 | 152 | 0.8 | 0.85 | 987 | 1248 | 1248 | 1498 | 1819 | 2055 | 2586 | 2810 |
| 0.8 | 0.9 | 216 | 274 | 274 | 329 | 399 | 451 | 568 | 618 | 0.8 | 0.95 | 81 | 103 | 103 | 124 | 151 | 170 | 215 | 233 |
| 0.85 | 0.9 | 747 | 945 | 945 | 1135 | 1377 | 1556 | 1958 | 2128 | 0.85 | 0.95 | 152 | 193 | 193 | 231 | 281 | 318 | 400 | 435 |
| 0.9 | 0.95 | 473 | 598 | 598 | 719 | 872 | 986 | 1241 | 1348 |
Contents of E:4
Contents of F:5
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